Ultra-High Throughput Detection Of Fluorescent Droplets Using Time Domain Encoded Optofludics

ABSTRACT

A high-throughput optofluidic device for detecting fluorescent droplets is disclosed. The device uses time-domain encoded optofluidics to detect a high rate of droplets passing through parallel microfluidic channels. A light source modulated with a minimally correlating maximum length sequences is used to illuminate the droplets as they pass through the microfluidic device. By correlating the resulting signal with the expected pattern, each pattern formed by passing droplets can be resolved to identify individual droplets.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/411,919, filed Oct. 24, 2016, the contents of which are incorporatedherein by reference in their entireties for all purposes.

FIELD OF THE INVENTION

The present invention relates to the detection of fluorescent dropletsusing time domain encoded optofluidics.

INTRODUCTION

Droplet-based assays, in which microscale emulsions are used as isolatedcompartments to run many independent chemical reactions, have generatedenormous enthusiasm in recent years as a platform for the ultrasensitivedetection of small molecules, proteins, and nucleic acids. Thesensitivity of droplet-based assays arises from the 10⁶× reduction ofthe microliter (μL) volumes of fluid used in conventional laboratoryassays to picoliter (pL) volumes. However, the enormous increase insensitivity that arises from massively parallelized, ultra-small volumeassays comes at the expense of requiring cumbersome instrumentation(pumps, optics, multiple microfluidic chips) and time-consumingprocessing (T>1 hour for current commercial systems) to generate,process, and measure millions of droplets. This processing time islimited by the inherently low throughput (10³ droplets/sec) in whichmicroscale droplets can be generated and fluorescently detected usingconventional techniques.

One promising direction to scale-up droplet production and detection hasbeen the development of platforms that make it possible to operate manymicrofluidic droplet generators and detectors in parallel. To this end,imaging platforms have been designed to measure many dropletssimultaneously. Alternatively, in-flow detection systems, can measure afar greater number of droplets than possible with the static techniques,and have the advantage that droplets can be sorted downstream of thedetector.

One microfluidic technique that has been developed in an attempt tosimplify detection without expensive lenses, cameras, and lasers usesspatial modulation to monitor parallel droplets or cells using a singlephotodetector. See, e.g., Muluneh et al., “Miniaturized, multiplexedreadout of droplet-based microfluidic assays using time-domainmodulations,” Lab Chip 14, 4638-46 (2014).

In another approach, a hybrid CMOS/microfluidic chip was reported thatcan detect droplets in-flow, in many parallel channels, achieving veryhigh throughput (254,000 droplets/sec). See Kim, M. et al., “Optofluidicultra-high-throughput detection of fluorescent drops,” Lab Chip 15,1417-1423 (2015). The method disclosed by Kim et al. requires aspecialized microfluidic device in which the microfluidic channels arefabricated directly on a CMOS chip and uses a high frame rate (2125 fps)to capture a four pixel-wide image that approximates the width of theindividual droplets.

However, these developments have been limited by their need forspecialized, expensive high frame rate cameras, are limited to measuringthe number of droplets that can be packed into a single image (<1million), limiting dynamic range, or require disposable microfluidicchips that incorporate expensive (>$10/chip) CMOS chips.

SUMMARY OF THE INVENTION

Aspects of the present invention relate to devices and methods fordetecting droplets using optofluidics.

A first aspect of the present invention relates to a method fordetecting fluorescent droplets comprising:

-   -   generating a plurality of droplets and flowing the droplets        through at least one channel of a microfluidic device;    -   illuminating the droplets with a time-domain modulated sequence        of flashes from a light source, wherein the time-domain        modulated sequence has a duration, d; and    -   capturing at least one image of the at least one channel,        wherein the image has an exposure time, t, greater than or equal        to the duration, d, of the time-domain modulated sequence.

A second aspect of the present invention relates to a device fordetecting fluorescent droplets comprising:

-   -   a droplet generator in a microfluidic channel;    -   a light source for illuminating droplets in the microfluidic        channel;    -   a controller for flashing the light source in a time-domain        modulated sequence; and        a photo sensor for capturing an image of the microfluidic        channel

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1a shows a conventional digital assay.

FIG. 1b shows a schematic representation of a parallel droplet generatorand fluorescent detection apparatus according to an embodiment of theinvention.

FIG. 1c shows a schematic representation of a cell phone camera beingused as the detector according to an embodiment of the invention.

FIG. 1d shows an image of a passing droplet with constant LEDillumination.

FIG. 1e shows a maximum length sequence (MLS) modulated excitationscheme according to an embodiment of the invention.

FIG. 1f shows an image of a passing droplet illuminated by the MLSmodulated excitation scheme of FIG. 1 e.

FIG. 1g shows the resolution of a sharp peak after correlating thesignal with the expected pattern resulting from the MLS modulationscheme shown in FIG. 1 e.

FIG. 2a shows a schematic representation of the workflow for analyzingan acquired fluorescence image according to an embodiment of theinvention.

FIG. 2b shows a schematic representation of the workflow for acquiringthe correlation vector according to an embodiment of the invention.

FIG. 2c shows a single frame of an image showing several dropletspassing through according to an embodiment of the invention.

FIG. 2d shows an enlarged portion of the frame shown in FIG. 2 c.

FIG. 2e is a schematic representation of the rolling shutter effectacquired in images according to an embodiment of the invention.

FIG. 2f is a schematic representation of a 3D matrix consisting of datathat has correlation intensities for phases and velocities to beexamined according to an embodiment of the invention.

FIG. 2g is a single 2D slice highlight at the proper phase with arrowspointing to the velocity that matches the droplet velocity.

FIG. 2h is a graph of several cross sections at varying velocitiesdemonstrating the sensitivity of the correlation function for the propervelocity.

FIG. 3a is a graph showing the ratio of the correlation with a channelcontaining a signal to one without a signal and the signal to noiseratio as a function of the number of bits according to an embodiment ofthe present invention.

FIG. 3b shows a simulation of two separate signals which were addedtogether and run through a correlation algorithm according to anembodiment of the present invention.

FIG. 3c shows a simulation of the signal of 20 droplets in a singlechannel and the resulting correlation according to an embodiment of thepresent invention.

FIG. 4a shows experimental data of adjacent channels according to anembodiment of the present invention.

FIG. 4b shows the resolution of a barely visible droplet passing througha noisy background according to an embodiment of the present invention.

FIG. 4c shows the ability to resolve overlapping droplets using an MLSmodulated sequence according to an embodiment of the present invention.

FIG. 4d shows the resolution of two droplets traveling at different flowrates according to an embodiment of the present invention.

FIG. 4e shows an image and results showing the phase independentrecovery of droplets despite the rolling shutter effect according to anembodiment of the present invention.

FIG. 5a shows raw images from a cell phone camera at two different framerates according to an embodiment of the present invention.

FIG. 5b shows a comparison of the DC SBR and AC correlation at the framerates of 30 and 60 fps to demonstrate the ability to resolve lowconcentrations of fluorescent dye according to an embodiment of thepresent invention.

FIG. 5c shows three frames as the droplet travels through and thecorresponding correlation heatmap according to an embodiment of thepresent invention.

FIG. 5d shows the dynamic range that can be covered by an embodiment ofthe present invention.

FIG. 5e shows droplets at various concentrations travelling through theframe according to an embodiment of the present invention.

FIG. 5f shows the resolution of the droplets appearing in the image ofFIG. 5 e.

FIG. 6a shows a graph demonstrating the variation in flow rate acrossdifferent channels at a given flow rate with error bars representing thestandard deviation according to an embodiment of the present invention.

FIG. 6b shows a graph of the droplet velocity recorded as a function ofthe flow rate with error bars showing the standard deviation at each ofthe given data points according to an embodiment of the presentinvention.

FIGS. 7a and 7b show the pincushion distortion caused by the macro lensused in accordance with an embodiment of the invention and thecorrection of the distortion using Matlab.

FIGS. 7c and 7D show the initial distortion and subsequent correction,respectively, according to an embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

Aspects of the invention are directed to devices and processes fordetecting fluorescent droplets in microfluidic devices.

The inventors have recognized that it would be useful to use inexpensivecameras to detect fluorescent droplets in microfluidic devices.Typically, inexpensive cameras are unable to resolve droplets inmicrofluidic devices because the speeds at which droplets travel throughmicrofluidic channels results in blurred images. Constant illuminationof the microfluidic channel results in images in which a fluorescentdroplet shows up as a blurred streak (FIG. 1d ). The blurred streakmakes it difficult or even impossible to resolve individual droplets, orto even determine whether multiple droplets are present. Although thedetection of droplets is discussed throughout the present disclosure andin the examples, it is understood that the processes and techniquesdescribed herein are also applicable to other discrete objects that maybe detected through imaging, such as cells and beads.

The inventors have recognized that individual droplets can be identifiedusing time-domain modulated sequences of light flashes. Rather thanusing constant illumination, the embodiments of the present inventionuse time-domain modulated sequences to illuminate the microfluidicchannels with a sequence of flashes (FIG. 1e ). The use of a time-domainmodulated sequence of flashes results in an image with a sequence ofblurred lines corresponding to the flashes of light (FIG. 1f ). Bycorrelating the captured image with the expected pattern, each patterncan be resolved into a sharp peak corresponding to an individual droplet(FIG. 1g ).

According to at least one embodiment of the present disclosure, themicrofluidic device may comprise any microfluidic device having achannel that can be imaged. The microfluidic device or chip can comprisea simple structure having one or more channels formed therein. Themicrofluidic device may comprise a droplet generator and at least onechannel through which the droplets may flow.

According to at least one embodiment, the microfluidic device comprisesa plurality of channels, each of which comprises a droplet generator.The throughput of the present device is linearly scalable with thenumber of channels. The channels may be dimensioned to accuratelycontrol the passage of the droplets. For example, the channels may havea width slightly larger than the diameter of the droplets to avoidclustering of the droplets. In some situations, however, it may bedesirable to use channels significantly larger than the diameter of thedroplet.

Inexpensive cameras can be used to capture images according toembodiments of the present invention. For example, such inexpensivecameras include the cameras on cellular phones or simple point-and-shootcameras. Such cameras typically use small complementarymetal-oxide-semiconductor (CMOS) photo sensor arrays. For cellularcameras, these CMOS photo sensors are typically on the order of a fewmillimeters per side (1/3.2″ diagonal). Larger photo sensors anddifferent types of photo sensors (e.g., CCD sensors) may be used toimprove the throughput and/or resolution. For example, point-and-shootcameras may have a sensor up to 1″ diagonal, a digital SLR camera mayhave a sensor up to 35 mm diagonal, and a medium format camera may havea photo sensor two to six times larger than a 35 mm “full frame” DSLR.The improved performance of larger sensors comes at a tradeoff ofportability and price.

In at least one embodiment, the photo sensor is capable of recordingimages or video at speeds of 30 frames per second (fps) or more, suchas, for example, 60 fps, 120 fps, 240 fps, or more. Increasing the framerate of the photo sensor generally increases the resolution andpotential throughput of the device.

The device may further comprise filters to enhance or improve the signalfrom the fluorescent droplets.

According to at least one embodiment, the light source is one which maybe precisely controlled at high speeds. For example, the light sourcemay comprise a light emitting diode (LED). In at least one embodiment,the light source may comprise more than one light source, such as, forexample, two or more LEDs that emit different wavelengths. Due to thephysical separation of the channels, it is possible to multiplex thedetection mechanism to detect a wide array of biomarkers simultaneouslyand track the abundance of each type using the channel separation.

Illumination of the light source can be controlled by a controller, suchas a microcontroller like the Arduino, an open-source electronicprototyping platform which allows users to control electronic objects.

The light source is illuminated using a sequence of flashes. Usingtime-domain modulated sequences, the inventors have recognized that itis possible to resolve individual droplets from images of dropletspassing through microfluidic channels, even when the droplets are movingat high speeds.

In at least one embodiment, the light source is flashed or blinked usingmaximum length sequences (MLSs). A maximum length sequence is apseudorandom binary sequence (e.g., signals the light source to turn onor off) typically used in digital telecommunications.

According to at least one embodiment, the MLS is a minimallycorresponding MLS. The minimally corresponding MLS has a sequence thatdiffers throughout the sequence, i.e., the duration of the signals for“on” and “off” changes throughout the sequence. In at least oneembodiment the MLS has a beginning sequence that differs from the endsequence. As used herein, the terms “beginning sequence” and “endsequence” refer to the portions of the MLS at the start of the sequenceand at the end of the sequence, such as, for example, the first half ofthe sequence and the second half of the sequence.

In at least one further embodiment, the MLS may also comprise a middlesequence that differs from the beginning sequence and the end sequence.As used herein, the term “middle sequence” refers to the portion of theMLS between the beginning sequence and the end sequence. For example,the middle sequence can be the middle third of the sequence and thebeginning and end sequences can be the outer thirds of the sequence.

As used herein, the term “differs” with respect to the time-domainmodulated sequences refers to the pattern and/or duration of portions ofthe sequence, e.g., the beginning sequence and the end sequence.

For example, the beginning and end sequences can differ in the patternof the signal, e.g., the duration of the on/off signals and the spacesbetween “on” signals. Similarly, the portions of the sequences candiffer based on the average duration of the signals.

In at least one embodiment, the portions of the sequence can havesimilar average duration of the signals but still differ based on thepattern of duration. For example, the beginning portion may comprise asequence of very short and very long “on” signals that have the sameaverage duration as an end sequence that comprises a series of mediumduration “on” signals.

In other embodiments, the portions of the sequence can differ in bothduration and pattern of the signals.

According to at least one embodiment, the portions of the signalsdiffers by at least 10%, such as at least 20%, at least 30%, at least40%, at least 50%, at least 60%, at least 70%, at least 80%, at least90%, or more. The percentage that one sequence differs from the othermay be measured by the amount of overlap of the sequence or by theaverage duration of the signals in the sequences.

According to at least one embodiment, the length of the time-domainmodulated sequence is less than the length of the exposure time of theacquired image. For example, if the image is a frame from a video shotat 30 fps, the length of the time-domain modulated sequence can be 1/30sec or less. Similarly, if the image is a frame from a video shot at 60fps, the length of the time-domain modulated sequence can be 1/60 sec orless. Although it is possible to use a sequence that is longer than theexposure time of the image, the resulting analysis may be more complex.

According to at least one embodiment, the image can be analyzed bycorrelating the acquired image with the expected image based on thetime-domain modulated sequence of light flashes. Because the sequence isknown, the small streaks of light corresponding by fluorescent materialin the droplets can be resolved to identify individual droplets.

The device according to embodiments of the present disclosure may be aportable device, e.g., using a portable camera, or a lab device.

Although the invention is illustrated and described herein withreference to specific embodiments, the invention is not intended to belimited to the details shown. Rather, various modifications may be madein the details within the scope and range of equivalents of the claimsand without departing from the invention.

EXAMPLES

The exemplary microdroplet Megascale Detector (pMD) in accordance withembodiments of the present disclosure may generate and detect thefluorescence of millions of droplets per second (1000× faster thanconventional approaches). By strobing the excitation light with apseudorandom time-domain modulated sequence, a 100× faster throughputthan possible using a continuous light source. The pMD measures dropletsat a rate of 10⁶ droplets/sec (ϕ=160 mL/hr) in 120 parallel microfluidicchannels, a limit of detection LOD<1 μM rhodamine dye, sufficient forconventional droplet based assays, and a dynamic range of 1/10⁷ to 1/30(positive/negative droplets). The exemplary pMD incorporates 120parallel droplet makers and only one set of oil, aqueous, and outputline. By miniaturizing and integrating droplet based diagnostics into ahandheld format, the pMD platform can translate the ultra-sensitivedroplet based assays now being developed in a self-contained platformfor practical use in clinical and industrial settings.

The μMD is a disposable microfluidic chip fabricated using only plasticand requires no active components, enabling extremely low costimplementation (<10¢/chip). In addition, since the chip is only used fordroplet flow, there is no need to sterilize the chip or functionalize itwith antibodies that need to be handled carefully. The example belowdemonstrates the use of conventional cell phone cameras to demonstratethe ability of droplet microfluidics to be used in point of careapplications due to the ubiquitous availability of smart phones, withtheir sensitive cameras and connection to cloud computing.

The μMD uses only a cell phone camera with commercial lenses that are<$4, and an LED that is modulated with an Arduino. The reusablecomponent of the μMD consists of a microfluidic chip made of only PDMSand glass, where each chip simply slides into a 3D printed casing thatautomatically aligns the chip and keeps the process extremelyinexpensive (FIG. 1c ). Unlike high frame rate recording devices, cellphone cameras are typically hindered by the low frame rate. The lowframe rate causes droplets to show up as streaks during recording (FIG.1d ), and is a limiting factor for the dynamic range where overlappingstreaks cannot be resolved as individual droplets. To solve this, thedroplets were patterned by blinking the LED in minimally correlatingMaximum Length Sequences (MLS) (FIG. 1e ), allowing each pattern inspace to be resolved into a sharp peak after correlating the signal withthe expected pattern (FIGS. 1f and 1g ). The μMD enabled dropletmicrofluidic detection with cell phone based cameras at extremely highthroughput (ϕ=100 mL/hr), sensitivity comparable to traditionaltechniques (1 μM), and a dynamic range that reflects the typical ratiosneeded for digital assays (1:10⁷ to 1:300).

Methods

Device Fabrication and Optics Design

The chip was fabricated using traditional soft lithography to make thefluidic channels that were then plasma bonded to a glass slide. The PDMSlayer was fabricated using single-layer SU-8 lithography (SU-8 2050,Microchem) with 50 μm thick features to accommodate for the 40 μmdroplets.

In order to keep the optics as inexpensive as possible, a commercialclip-on macro lens was used in series with a longpass filter fitteddirectly into an acrylic casing to keep a small-footprint product. Anultra-bright LED (λex=530 nm) (Luminus, CBT-90-G-C11-JK201) provided theexcitation light, which was connected to an LED driver (LuminusDevelopment Kit, DK-114N-3). The development kit allowed LED modulationup to 40 kHz using standard TTL response to turn the LED on or off basedon a user generated signal from an Arduino Mega2560. The housing for themicrofluidic chip was designed such that the PDMS chip had maximumanti-resonant coupling to ensure the excitation light spread evenlythroughout the entire chip. A 605 nm long pass filter (Edmund Optics,#52-528) was placed between the chip and lens to diminish (95%reflectance) the effect of scattered light at the excitation wavelength.Finally, an inexpensive (<$4), 15× commercially available macro lens(Carson HookUpz, ML-515) was placed directly in front of the cell phonecamera such that the spacing between the microfluidic channels and thelens was 0.5 inches. Due to spherical aberration from this lens,software was to undistort images rather than resort to expensive opticalsolutions (FIG. 7). A checkerboard pattern was used to calibrate thedistortion, and then MATLAB's Computer Vision System Toolbox was used toundistort the droplet images to transform the pincushion aberration intoa flat 2d image where the channels be segmented properly.

Cell Phone Parameters

Unlike traditional scientific CMOS camera where the user is in controlof most image acquisition parameters, a cell phone camera has only ahandful of features that need to be optimized prior to recording. Usinga Samsung Galaxy S7 Edge's Camera “Pro” Mode, the following settingswere used to record: (i) the focus was manually fixed so the chip couldslide in to an acrylic casing without having to align the chip, (ii) theISO was set to 3200 and Exposure to +2 maximize light input unlessspecified otherwise, (iii) aperture was set to 1/30, (iv) metering modewas set to Matrix, and (v) the color correction was set to Auto. Allvideos were recorded 1920×1080p size at 60 fps or 30 fps using theOpenCamera App, since this setting captured all 120 channels properlywithout extremely large file sizes, with a field of view of ˜12 mm by 7mm. While higher resolution videos could be captured, this would havecreated file sizes that would take much longer to analyze withoutsignificantly increasing the field of view. The video was thentransferred to a personal computer to analyze for droplet detection. Allanalysis was done in MATLAB on a personal computer, but cloud computingcould be used for a complete portable implementation.

LED Modulation and Streak Patterning Design

The LED modulation sequence was selected such that: (i) patterns wereminimally correlated with themselves and (ii) the lengths of the patternwere long enough to create sharp peaks within the number of pixels setby the length of the imaging region (1920 px). To generate thesepatterns, a mathematical framework for applications in radar andtelecommunications developed to generate maximally uncorrelated,pseudorandom sequences was used.

The length of the mask patterns for the device that we designed was 63bits. The sequence was made as long as possible to minimize eachchannel's autocorrelation. The length was constrained by the camera'sability resolve bits and the length of the imaging region. The SamsungGalaxy S7 used had a detection region of 7×12 mm². The size of each“bit” in the sequence was set to ˜10 pixels on the CMOS detection regionwhile recording at 1080p (1920×1080 pixels). Based on this selection, afull droplet streak with the chosen velocity would correspond to ˜⅓ ofthe frame. The generated MLS from MATLAB was then exported and loadedinto the Arduino Mega, which controlled when the LED was on/off. Forexample, if a 63-bit MLS was desired for a passing droplet at 60 fpsrecording, the MLS was repeated every ˜17 ms, with each bit consistingof Δt=˜0.25 ms. Thus, the number of time spent on each bit can be givenby the equation:

$t_{bit} = \frac{1s}{{frame}\mspace{14mu}{rate}*N_{bits}}$

It was confirmed that the Arduino was able to modulate the LED at thisrate by measuring the output from the Arduino with a NI DAQ board withsampling rate of 1 GHz (NI SCB-68A/USB X Series). It was also verifiedthat the LED response matched the Arduino MLS output pattern using a SiPhotodetector (PDA100A, Thorlabs) to ensure that the LED and Arduinowere able to properly output the desired temporal MLS.

Results

Due to the low frame rate of the cell phone camera (<240 fps), a passingfluorescent droplet looks like a streak since multiple pixels areexposed to the passing light during the time a droplet passes across thedetection region (FIG. 1e ). However, when multiple fluorescent dropletspass together, it becomes impossible to differentiate the droplets fromeach other. Without the ability to resolve multiple droplets passing inclose proximity, the dynamic range for the system is severely limited,requiring samples to be diluted to large volumes and increaseinterrogation time or use samples with a confined dynamic range. Thus,time encoding serves two purposes: (i) resolve overlapping droplets assharp peaks through correlation that can be resolved into individualdroplets, and (ii) recover signals below the noise floor.

Signal Extraction

Briefly, the algorithm workflow of the system was as follows: (i) avideo stream recorded of the droplets was reviewed frame by frame; (ii)each channel was partitioned into channels by the physical location ofthe pixels of the CMOS camera (FIG. 2c, 2d ), (iii) for each givenchannel, one phase was first picked and several velocities were scannedthrough for the given phase, and optimal velocity for the given phasewas found (iv). The strongest fitting correlation peak was recovered foreach channel based on the appropriate velocity and phase of thedroplet's pattern after removing any offset from noise with a high passfilter (FIG. 2b ).

A single frame is shown with several droplets passing (FIG. 2c ). Thenumber of pixels that fit on a camera frame at 1920×1080p gives a widthconsisting of 1920 pixels for one channel and a length of 1080 px, for afield of view corresponding to 12 mm×7 mm. For 120 channels, eachchannel was partitioned into 1080 pixels/120˜9 pixels/channel andchannels were thus defined based on the physical mapping of the imageonto the CMOS sensor (FIG. 2d ). However, the camera does not take anentire image at once, but instead records the intensities for eachrow—termed the rolling shutter effect. This lag between the time ofexposure and readout caused a delay between the different rows on theCMOS sensor. The blinking LED did not create a single pattern for allthe rows in a given frame, but instead caused the pattern to be phaseshifted based on this readout offset for each row (FIG. 2e ). Thus, theresulting pattern to be identified for each droplet could be phaseshifted from the rolling shutter effect and from the setup where the LEDand cell phone are not perfectly synced for each frame. Therefore, whiledroplets at a given flow rate traveled at a narrow range of velocities(CV=4.4% at 100 mL/hr) (FIG. 6), it was necessary to identify theoptimal phase and velocity before detecting a droplet.

In order to identify the “correct” correlation, a 3d matrix was firstgenerated consisting of data that had correlation intensities for theall phases and velocities to be examined (FIG. 2f ). In this case, fiveslices of separate phases are shown, and the correct phase of thedroplet is highlighted in red. For each phase, the range of velocitiesthat are appropriate were examined. To demonstrate the sensitivity tothe velocity, a velocity scan from 375 px/frame to 1229 px/frame with240 increments in between was shown. At a given velocity, a sharp peakin the heatmap on the 2D slice is observed, demonstrating that at thechosen phase, the droplet velocity matches a velocity of 493 px/frame.In reality, the number of velocities and phases that were scannedthrough were at a smaller range to improve computational efficiency.

Characterizing the Effect of Design Parameter Choices on Performance

To aid in the design and characterization of the device, severalsimulations were performed in MATLAB. The two questions investigatedwere: (i) how many bits in a pattern can be used to separate the signalfrom the background at a level where the Signal to Noise Ratio (SNR)=1and (ii) how close can overlapping droplets exist before the peak fromthe correlation begins to overlap with the neighboring fluorescentdroplet, making droplets undistinguishable. Noise was defined asGaussian white noise that was added to the system.

In order to simulate the first question, mask patterns of various MLSwere generated with different number of “bits” corresponding to thelength of the MLS. The length of the MLS ranged from 10 bits to 100bits. Each MLS was given a width of 10 pixels to model the experimentalsetup, thus a 50 bit MLS was 500 pixels long. The correlation of avector with (Wsig) and without (LPbg) a signal was compared at variousnoise levels the number of bits were scanned through. The root meansquare (RMS) of the resulting correlation vectors around their peaks wastaken, and this ratio was defined as the correlation signal tobackground ratio (CSBR=Wsig/Lbg). FIG. 3a shows the resulting CSBR as afunction of both the number of bits and the SNR. Taking a vertical cutline at an SNR of 0 dB demonstrates how the CSBR increased as the numberof bits was increased (FIG. 3a -Top inset). Taking a horizontal cutlineshows that as we increase the number of bits, we can reach lower levelsof resolvable SNR. This simulation showing that even when the SNR=0 dB,we are still able to resolve the CSBR 20 dB, demonstrating the power ofusing correlation for faint signals where noise may lead to falsepositives or false negatives otherwise.

Based on this simulation and physical constraints set by the number ofpixels in a given frame, 63 bits was selected for the MLS. Whilerecording in 1920×1080p, this corresponds to the droplet taking up to630 px/1920 px, or ⅓ of the frame. To find out how closely packed twodroplets can be, two separate droplets were simulated, and theirresulting signals added to create a sum consisting of the first andsecond droplet (FIG. 3b ). The resulting correlation showed two sharplydefined peaks that were 45 pixels apart (FIG. 3b ), and moving thesedroplets closer would result in these peaks beginning to overlap.

How many droplets that could be packed in a given frame was thensimulated, assuming they would be spaced in a sequential manner buttheir entry into the channel would be random to a degree where thedroplets were not exactly evenly spaced. Each of the individual dropletswhen combined would create a signal that would be below the 256 RGBlimit on the cell phone, and the resulting sum of the 20 droplets showshow the resulting signal looks impossible to identify the twentypatterns (FIG. 3c ). By taking the correlation with the expectedpattern, it was shown that the resulting correlation can recover the 20separate droplets amidst this chaos.

Experimental Validation:

In order to validate the system, several features were highlighted thatdemonstrated the μMD's capability to identify droplets across a widerange of velocities, phase shifts, dynamic range, and fluorophoreconcentration.

It was first shown that neighboring channels could be partitionedeffectively into two separate segments based on the physical mapping ofthe camera. The fluorescent signal from a given channel did not leechinto neighboring channels and affect the resulting correlation in nearbychannels (FIG. 4a ). To demonstrate that signals can be resolved evenwhen they are comparable to the noise, a droplet with 500 nM fluorescentmaterial was shown passing through the channel whose streak pattern isbarely resolvable. However, when taking the correlation, a sharp peakseparated from the background was seen allowing us to extract diluteconcentrations of dye with comparable sensitivities to other dropletdetector devices (FIG. 4b ). Correlation not only resolved low signaldroplets but also allowed separation of droplets that were overlapping.FIG. 4c shows two droplets where the pattern overlapped, but afterrunning the algorithm two peaks were defined for each of the separatedroplets.

To demonstrate the ability to scan through a wide range of velocities,two separate droplet signals were shown from two separate flow rates(FIG. 4d ), one with a velocity of 400 px/frame and another with avelocity of 1000 px/frame. After scanning a set of velocities from 366px/frame to 1229 px/frame which are chosen arbitrarily based on the dataset, the ability to identify droplets even with a wide range ofvelocities was demonstrated. In reality, the velocity distribution ofdroplets was constrained so the scan for velocities was limited to anarrower range (for example, if droplets are designed to cover ⅓ of theframe, velocities from 500 to 700 px/frame can be scanned).

As mentioned, the rolling shutter effect and the mismatch of the LED MLScycle with the frame rate capture of the cell phone caused an offset inthe phase of the pattern. FIG. 4e shows three separate channels withdifferent initial phases due to these effects. The initial phase foreach droplet is highlighted above according to the user generated MLSuploaded to the Arduino. After scanning through phase for each of thesepatterns, it was possible to properly identify each droplet despitephase offsets.

Quantification of Device Sensitivity and Dynamic Range

In order to quantify the sensitivity of the device, a serial dilutionwas performed with dye to achieve a limit of detection down to 1 μM—aconcentration limit below which biological assays are performed. 40 μmdroplets were generated containing Dextran Rhodamine B 10,000 MW with0.15 M MgSO4 (Thermo, D1824) for the dispersed phase and a continuousphase consisting of 0.65 cst Silicone Oil (Consolidated Chemical) with5% v/v Span80. The dye was diluted at various concentrations and thesignal to background ratio was measured using two methods. 1) First, theSBR was measured with the raw image only in the “R channel” of the RGBmatrix, where the ratio of the droplet signal to the signal from anempty channel was taken and this was called the raw signal to backgroundratio (RSBR). 2) The background was then subtracted and the algorithmrun. The correlation of a channel with a droplet and a channel without adroplet was taken, and the energy around the peak of the droplet wastaken and divided by the energy of the background. This was called thecorrelation signal to background ratio (CSBR).

Raw images of the droplets at various concentrations and frame rates areshown (FIG. 5a ), along with the RSBR and CSBR are plotted (FIG. 5b ).Lower frame rates had a higher exposure time, allowing lowerconcentrations of dye to be detectable. Both followed a linear fit asexpected, but as lower concentrations of dye were reached, the RSBRbegan to reach a ratio of 1 and was inseparable from the background.However, the CSBR was able to recover these low signal droplets andseparate them from noise since the background noise did not contributeto any peaks in the background correlation vector. Using correlation, itwas possible to resolve concentrations down to 1 μM that wereoverwhelmed by noise in the raw DC regime. While the RSBR flat lined toa ratio of 1, the reason we could not reach even lower concentrations ofdye with correlation were due to the digitization effect of the CMOScamera. Since the RGB values only range from 0-256, we began to hit thedigitization limit before lower concentration of dyes could be resolved(FIG. 5a ).

Quantification of Dynamic Range

In order to quantify the dynamic range of the device, the capabilitieswere tested by spiking in a known number of fluorescent droplets into aknown number of empty droplets. The droplets were then pipetted slowlyto mix the sample to distribute the fluorescent droplets evenly amongthe empty droplets. Due to the ultra-high throughput detection speed, itwas possible to sample large volumes of droplets to reach sensitivitiesthat cannot be afforded by typical systems. Since most digital assaysuse ˜μL scale volumes, they are often limited to the maximum sensitivityof rare targets. For example, Biorad's 20,000 droplets of 20 μL ofsample allow for a detection of 50 targets in 1 mL, if 1:20,000 dropletsare positive leading to high variability. Here, it was shown that withthe ability to process large samples rapidly it was possible to imagedroplets as low as 1 in 10⁷<5 minutes to enable detection of extremelyrare genes in a large background population (KRAS gene in pancreaticcancer), or sparse sample in a large volume (HIV monitoring for patientson treatment).

The dynamic range tested ranged from 3 fluorescent droplets to 10⁶fluorescent droplets in 1 mL of oil (this was tested in ˜3*10⁷ emptydroplets, which is equal to 1 mL of sample). The empty dropletscorrespond to 1 mL of empty sample that were converted into 40 μmdiameter droplets. Both sets of droplets were made on flow-focusingdevices to create a narrow CV for the droplet diameters to account forany variation from droplet volume size. In order to make the titrations,1 mL of fluorescent dye were used to create ˜3*10⁷ droplets, and a knownvolume based of this starting amount was spiked into each of thesyringes that were run. Due to variations in the spiked sample vsobserved due to droplet sedimentation, droplet concentrations wereverified by measuring the percent of positive droplets on a fluorescentmicroscope (Leica) as well as visually counting the droplets in thevideo for the low concentrations.

In order to count droplets as they moved through the frame, theresulting correlation vectors were used for each of the channels tocalculate the number of true droplets (FIG. 5c ). Since a droplet couldpotentially peak two or three times based on its entry into the channel,repeat peaks were removed based on the reoccurrence of the correlationmax if it repeated in an expected location in the next one or twoframes.

The requirement to have at least two correlation peaks in successiveframes based on the droplet's velocity also removed any erroneous peaksthat may exist from noise, and thus the system was both robust to peaksfrom background and did not over count droplets. A linear fit was shownbetween the number of the spiked number of fluorescent droplets and themeasured ones for each set (FIG. 5d ). FIG. 5e shows raw images ofseveral channels at different concentrations, while showing the abilityto resolve highly overlapping droplets despite the frame lookingovercrowded with overlapping patterns (FIG. 5f ). The data followed alinear fit as expected, and variations from the linear fit could beattributed to Poisson error.

It was demonstrated that due to the extremely parallelized detectionscheme, 108 droplets could be observed in as little as three minutes, a1000-fold reduction compared to the commercial systems. The upper limitof the dynamic range corresponded to when overlapping signals fromfluorescent droplets became unresolvable. The lower limit couldtheoretically be further improved by running more samples, but 3droplets/mL corresponds to extremely rare events that were believed tobe sufficient for most digital assays.

DISCUSSION

Dynamic Range Vs Throughput:

It was shown that simulations and experimental data matched closely.Furthermore, the number of fluorescent droplets that fill a channel atthis concentration corresponded to ˜2% positive droplets in the channelat a given time. Dynamic range is defined as the ability to detectdroplets that are positive with fluorescent dye, whereas throughput isdefined as the total volume that can be processed through the devicebased on the flow rate. Positive droplets signify the number of dropletscontaining a biological target since in digital assays, positivedroplets fluoresce larger. In many digital assays, most targets are inrare abundance, which is the reason digital assays are so powerful inincreasing the concentration of the target within a confined volume.

For throughput, it was found that when a cream of droplets alone withminimal oil spacing was passed that the droplets would often clusteraround the channel inlet and outlet and could clog the channel entrance.This clogging prevents the velocity distribution spread to be even, sospacing oil was added between the droplets. Spacing oil is often aresult of droplet generation where the oil flow rate is larger than theaqueous flow rate, creating naturally gaps. While work has been done toeliminate spacing oil, it was found that spacing oil could help allowdroplets to travel through without clogging the channels. When thedroplet fraction compared to the total volume was <50%, minimal cloggingand variations on the velocity distribution were observed. Whilenegative droplets can be useful to count in the total droplets detectedper sample, since most the droplets are empty and droplet formationcreates droplets that are monodisperse, the total droplets that havepassed can be approximated by the known droplet diameter. For example,if 1 mL of sample is run, the number of droplets based on their diameterare [35 μm, 4.45*10⁷], [40 μm, 3*10⁷], or [45 μm, 2*10⁷]. Thus, for raredetection where the targets are less than 105 copies/mL, the variationfrom this assumption of fraction of fluorescent droplets are [0.225%,0.3351%, 0.447%], respectively.

Effect of droplet diameter and pattern: Droplets that are much largerthan 40 μm tended to squish through the channels of imaging. As thesedroplets squeeze through, they tended to change shape into ovals thatspread out, and the time modulation began to see a smoothing effectbased on this oval shape. Rather than seeing droplets as a point, theemission began to overlap with regions that should have been “off” andaffected the pattern. Droplets that were much smaller than the 40 μmwidth of the channel did act as points, but since the intensity scalesin a cubic manner with the droplet diameter, droplets that are reallysmall tend to emit too little light to be detected by the cell phone.

Tradeoff between design parameters: There were several constrains wepreviously described for selecting design considerations such as dropletstreak length, number of bits etc. However, there are several othervariables designed based on the scenario. One selection was the framerate of the cell phone camera and the limit of detection effect. As theframe rate of the camera was increased, it was possible to increase thethroughput since more droplets could be analyzed. However, as the framerate was increased, there was less exposure for the CMOS, so the limitof detection decreased. Therefore, a lower fps was used for detectingsensitive assays.

Next, as the flow rate increased, the streaks became larger and thetotal intensity of the droplet spread over the streak length. If theflow rate was too slow, the streaks became shorter and it was difficultto resolve the patterns, while if the total streak exceeded 1920 px, theentire pattern did not fit on the frame. Therefore, for each frame rate,the flow rate was selected such that each droplet took about ⅓ of thescreen—both for counting the droplets as they moved frame by frame andso that their lengths were large enough to contain enough bits for thepatterning.

In addition, there are several camera settings that could be changed toalter the video acquisition. High ISO for bright droplets can lead tosaturation too early making overlapping droplets harder to resolve sincethe pattern becomes saturated. For most biological assays, this shouldnot be an issue since it is unusual to measure droplets withconcentrations of 100 μM or more.

A cloud based server could also be used for calculations. In theexamples above, data was taken off the smartphone and processed inMatlab on a desktop computer. By beaming the data to a server forprocessing, the user could directly receive the droplet counts on theirsmartphone. While parallel computing in Matlab was efficient with theparallel computing toolbox, the processing may be further optimized byswitching to a vectorized program that can process the correlationcomputations needed faster.

1-20. (canceled)
 21. A device comprising: a light source configured toilluminate a plurality of droplets passing through a microfluidicchannel; a controller configured to flash the light source in atime-domain modulated sequence; and a photo sensor configured to capturean image of the microfluidic channel.
 22. The device of claim 21,wherein the light source is a light-emitting diode (LED).
 23. The deviceof claim 21, wherein the photo sensor is a complementarymetal-oxide-semiconductor (CMOS) photo sensor.
 24. The device of claim23, wherein the CMOS photo sensor is part of a DSLR camera, apoint-and-shoot camera, a cellular phone camera, or a medium formatcamera.
 25. The device of claim 23, wherein the CMOS photo sensor is atleast 1/3.2″ diagonal.
 26. The device of claim 21, wherein thetime-domain modulated sequence has a duration, d, wherein the image hasan exposure time, t, and wherein t is greater than or equal to d. 27.The device of claim 21, wherein the time-domain modulated sequence is apseudorandom sequence.
 28. The device of claim 21, wherein thetime-domain modulated sequence is a minimally correlating maximum lengthsequence.
 29. The device of claim 28, wherein the maximum lengthsequence is 1/30 sec or less.
 30. The device of claim 29, wherein themaximum length sequence is 1/60 sec or less.
 31. The device of claim 21,wherein the photo sensor is configured to capture a video of themicrofluidic channel.
 32. The device of claim 31, wherein the video hasa frame rate of at least 30 fps.